from warnings import filterwarnings
filterwarnings('ignore')
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_csv('training_set.csv')
df.head()

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	2	2008	WD	Normal	208500
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	...	0	NaN	NaN	NaN	0	5	2007	WD	Normal	181500
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	9	2008	WD	Normal	223500
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml	140000
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	...	0	NaN	NaN	NaN	0	12	2008	WD	Normal	250000

5 rows × 81 columns

df.shape

(1460, 81)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1460 non-null   int64  
 1   MSSubClass     1460 non-null   int64  
 2   MSZoning       1460 non-null   object 
 3   LotFrontage    1201 non-null   float64
 4   LotArea        1460 non-null   int64  
 5   Street         1460 non-null   object 
 6   Alley          91 non-null     object 
 7   LotShape       1460 non-null   object 
 8   LandContour    1460 non-null   object 
 9   Utilities      1460 non-null   object 
 10  LotConfig      1460 non-null   object 
 11  LandSlope      1460 non-null   object 
 12  Neighborhood   1460 non-null   object 
 13  Condition1     1460 non-null   object 
 14  Condition2     1460 non-null   object 
 15  BldgType       1460 non-null   object 
 16  HouseStyle     1460 non-null   object 
 17  OverallQual    1460 non-null   int64  
 18  OverallCond    1460 non-null   int64  
 19  YearBuilt      1460 non-null   int64  
 20  YearRemodAdd   1460 non-null   int64  
 21  RoofStyle      1460 non-null   object 
 22  RoofMatl       1460 non-null   object 
 23  Exterior1st    1460 non-null   object 
 24  Exterior2nd    1460 non-null   object 
 25  MasVnrType     588 non-null    object 
 26  MasVnrArea     1452 non-null   float64
 27  ExterQual      1460 non-null   object 
 28  ExterCond      1460 non-null   object 
 29  Foundation     1460 non-null   object 
 30  BsmtQual       1423 non-null   object 
 31  BsmtCond       1423 non-null   object 
 32  BsmtExposure   1422 non-null   object 
 33  BsmtFinType1   1423 non-null   object 
 34  BsmtFinSF1     1460 non-null   int64  
 35  BsmtFinType2   1422 non-null   object 
 36  BsmtFinSF2     1460 non-null   int64  
 37  BsmtUnfSF      1460 non-null   int64  
 38  TotalBsmtSF    1460 non-null   int64  
 39  Heating        1460 non-null   object 
 40  HeatingQC      1460 non-null   object 
 41  CentralAir     1460 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1460 non-null   int64  
 44  2ndFlrSF       1460 non-null   int64  
 45  LowQualFinSF   1460 non-null   int64  
 46  GrLivArea      1460 non-null   int64  
 47  BsmtFullBath   1460 non-null   int64  
 48  BsmtHalfBath   1460 non-null   int64  
 49  FullBath       1460 non-null   int64  
 50  HalfBath       1460 non-null   int64  
 51  BedroomAbvGr   1460 non-null   int64  
 52  KitchenAbvGr   1460 non-null   int64  
 53  KitchenQual    1460 non-null   object 
 54  TotRmsAbvGrd   1460 non-null   int64  
 55  Functional     1460 non-null   object 
 56  Fireplaces     1460 non-null   int64  
 57  FireplaceQu    770 non-null    object 
 58  GarageType     1379 non-null   object 
 59  GarageYrBlt    1379 non-null   float64
 60  GarageFinish   1379 non-null   object 
 61  GarageCars     1460 non-null   int64  
 62  GarageArea     1460 non-null   int64  
 63  GarageQual     1379 non-null   object 
 64  GarageCond     1379 non-null   object 
 65  PavedDrive     1460 non-null   object 
 66  WoodDeckSF     1460 non-null   int64  
 67  OpenPorchSF    1460 non-null   int64  
 68  EnclosedPorch  1460 non-null   int64  
 69  3SsnPorch      1460 non-null   int64  
 70  ScreenPorch    1460 non-null   int64  
 71  PoolArea       1460 non-null   int64  
 72  PoolQC         7 non-null      object 
 73  Fence          281 non-null    object 
 74  MiscFeature    54 non-null     object 
 75  MiscVal        1460 non-null   int64  
 76  MoSold         1460 non-null   int64  
 77  YrSold         1460 non-null   int64  
 78  SaleType       1460 non-null   object 
 79  SaleCondition  1460 non-null   object 
 80  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(35), object(43)
memory usage: 924.0+ KB

df.describe()

	Id	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	...	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	SalePrice
count	1460.000000	1460.000000	1201.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1452.000000	1460.000000	...	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000	1460.000000
mean	730.500000	56.897260	70.049958	10516.828082	6.099315	5.575342	1971.267808	1984.865753	103.685262	443.639726	...	94.244521	46.660274	21.954110	3.409589	15.060959	2.758904	43.489041	6.321918	2007.815753	180921.195890
std	421.610009	42.300571	24.284752	9981.264932	1.382997	1.112799	30.202904	20.645407	181.066207	456.098091	...	125.338794	66.256028	61.119149	29.317331	55.757415	40.177307	496.123024	2.703626	1.328095	79442.502883
min	1.000000	20.000000	21.000000	1300.000000	1.000000	1.000000	1872.000000	1950.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	2006.000000	34900.000000
25%	365.750000	20.000000	59.000000	7553.500000	5.000000	5.000000	1954.000000	1967.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.000000	2007.000000	129975.000000
50%	730.500000	50.000000	69.000000	9478.500000	6.000000	5.000000	1973.000000	1994.000000	0.000000	383.500000	...	0.000000	25.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.000000	2008.000000	163000.000000
75%	1095.250000	70.000000	80.000000	11601.500000	7.000000	6.000000	2000.000000	2004.000000	166.000000	712.250000	...	168.000000	68.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.000000	2009.000000	214000.000000
max	1460.000000	190.000000	313.000000	215245.000000	10.000000	9.000000	2010.000000	2010.000000	1600.000000	5644.000000	...	857.000000	547.000000	552.000000	508.000000	480.000000	738.000000	15500.000000	12.000000	2010.000000	755000.000000

8 rows × 38 columns

df.columns

Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',
       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',
       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',
       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',
       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',
       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',
       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',
       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',
       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',
       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',
       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',
       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',
       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',
       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',
       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',
       'SaleCondition', 'SalePrice'],
      dtype='object')

s = df.isna().sum()
s[s>0]

LotFrontage      259
Alley           1369
MasVnrType       872
MasVnrArea         8
BsmtQual          37
BsmtCond          37
BsmtExposure      38
BsmtFinType1      37
BsmtFinType2      38
Electrical         1
FireplaceQu      690
GarageType        81
GarageYrBlt       81
GarageFinish      81
GarageQual        81
GarageCond        81
PoolQC          1453
Fence           1179
MiscFeature     1406
dtype: int64

EDA (Exploratory data Analysis)

df = df.drop(columns=['Id'])
df.head()

	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	...	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition	SalePrice
0	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	...	0	NaN	NaN	NaN	0	2	2008	WD	Normal	208500
1	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	...	0	NaN	NaN	NaN	0	5	2007	WD	Normal	181500
2	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	...	0	NaN	NaN	NaN	0	9	2008	WD	Normal	223500
3	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	...	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml	140000
4	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	...	0	NaN	NaN	NaN	0	12	2008	WD	Normal	250000

5 rows × 80 columns

cat = list(df.columns[df.dtypes=='object'])
con = list(df.columns[df.dtypes!='object'])

cat

['MSZoning',
 'Street',
 'Alley',
 'LotShape',
 'LandContour',
 'Utilities',
 'LotConfig',
 'LandSlope',
 'Neighborhood',
 'Condition1',
 'Condition2',
 'BldgType',
 'HouseStyle',
 'RoofStyle',
 'RoofMatl',
 'Exterior1st',
 'Exterior2nd',
 'MasVnrType',
 'ExterQual',
 'ExterCond',
 'Foundation',
 'BsmtQual',
 'BsmtCond',
 'BsmtExposure',
 'BsmtFinType1',
 'BsmtFinType2',
 'Heating',
 'HeatingQC',
 'CentralAir',
 'Electrical',
 'KitchenQual',
 'Functional',
 'FireplaceQu',
 'GarageType',
 'GarageFinish',
 'GarageQual',
 'GarageCond',
 'PavedDrive',
 'PoolQC',
 'Fence',
 'MiscFeature',
 'SaleType',
 'SaleCondition']

con

['MSSubClass',
 'LotFrontage',
 'LotArea',
 'OverallQual',
 'OverallCond',
 'YearBuilt',
 'YearRemodAdd',
 'MasVnrArea',
 'BsmtFinSF1',
 'BsmtFinSF2',
 'BsmtUnfSF',
 'TotalBsmtSF',
 '1stFlrSF',
 '2ndFlrSF',
 'LowQualFinSF',
 'GrLivArea',
 'BsmtFullBath',
 'BsmtHalfBath',
 'FullBath',
 'HalfBath',
 'BedroomAbvGr',
 'KitchenAbvGr',
 'TotRmsAbvGrd',
 'Fireplaces',
 'GarageYrBlt',
 'GarageCars',
 'GarageArea',
 'WoodDeckSF',
 'OpenPorchSF',
 'EnclosedPorch',
 '3SsnPorch',
 'ScreenPorch',
 'PoolArea',
 'MiscVal',
 'MoSold',
 'YrSold',
 'SalePrice']

Univariate Analysis

for i in cat:
    df[i].value_counts().plot(kind='bar',
                              title=f'Countplot for {i}',
                              figsize=(16,8))
    plt.show()

for i in con:
    sns.histplot(data=df, x=i, kde=True)
    plt.title(f'Histogram for {i}')
    plt.show()

Bivariate Analysis

Continuous vs Continuous

for i in con:
    if i!='SalePrice':
        plt.figure(figsize=(16,8))
        sns.scatterplot(data=df, x=i, y='SalePrice')
        plt.title(f'Scatterplot for {i} vs SalePrice')
        plt.show()

Categorical vs Continuous

for i in cat:
    plt.figure(figsize=(16,8))
    sns.boxplot(data=df, x=i, y='SalePrice')
    plt.title(f'Boxplot for {i} vs SalePrice')
    plt.show()

Categorical vs Categorcal (Cross Tab)

cat

['MSZoning',
 'Street',
 'Alley',
 'LotShape',
 'LandContour',
 'Utilities',
 'LotConfig',
 'LandSlope',
 'Neighborhood',
 'Condition1',
 'Condition2',
 'BldgType',
 'HouseStyle',
 'RoofStyle',
 'RoofMatl',
 'Exterior1st',
 'Exterior2nd',
 'MasVnrType',
 'ExterQual',
 'ExterCond',
 'Foundation',
 'BsmtQual',
 'BsmtCond',
 'BsmtExposure',
 'BsmtFinType1',
 'BsmtFinType2',
 'Heating',
 'HeatingQC',
 'CentralAir',
 'Electrical',
 'KitchenQual',
 'Functional',
 'FireplaceQu',
 'GarageType',
 'GarageFinish',
 'GarageQual',
 'GarageCond',
 'PavedDrive',
 'PoolQC',
 'Fence',
 'MiscFeature',
 'SaleType',
 'SaleCondition']

ctab1 = pd.crosstab(df['ExterQual'], df['ExterCond'])
ctab1

ExterCond	Ex	Fa	Gd	Po	TA
ExterQual
Ex	1	0	3	0	48
Fa	0	6	0	0	8
Gd	0	0	35	0	453
TA	2	22	108	1	773

sns.heatmap(ctab1, fmt='d', annot=True)

<Axes: xlabel='ExterCond', ylabel='ExterQual'>

Pipeline Creation

x = df.drop(columns=['SalePrice'])
y = df[['SalePrice']]

x.head()

	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	LotConfig	...	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	Inside	...	0	0	NaN	NaN	NaN	0	2	2008	WD	Normal
1	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	FR2	...	0	0	NaN	NaN	NaN	0	5	2007	WD	Normal
2	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	Inside	...	0	0	NaN	NaN	NaN	0	9	2008	WD	Normal
3	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	Corner	...	0	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml
4	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	FR2	...	0	0	NaN	NaN	NaN	0	12	2008	WD	Normal

5 rows × 79 columns

y.head()

	SalePrice
0	208500
1	181500
2	223500
3	140000
4	250000

Seperate Cat and Con for x

cat = list(X.columns[x.dtypes=='object'])
con = list(X.columns[x.dtypes!='object'])

from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from sklearn.compose import ColumnTransformer

num_pipe1 = Pipeline(steps=[('impute', SimpleImputer(strategy='median')),
                            ('scaler', StandardScaler())])

cat_pipe1 = Pipeline(steps=[('impute', SimpleImputer(strategy='constant', fill_value='NotAvailable')),
                            ('ordinal', OrdinalEncoder())])

pre1 = ColumnTransformer([('num', num_pipe1, con),
                          ('cat', cat_pipe1, cat)])

X_pre = pre1.fit_transform(x)
X_pre

array([[ 0.07337496, -0.22087509, -0.20714171, ...,  1.        ,
         8.        ,  4.        ],
       [-0.87256276,  0.46031974, -0.09188637, ...,  1.        ,
         8.        ,  4.        ],
       [ 0.07337496, -0.08463612,  0.07347998, ...,  1.        ,
         8.        ,  4.        ],
       ...,
       [ 0.30985939, -0.1754621 , -0.14781027, ...,  3.        ,
         8.        ,  4.        ],
       [-0.87256276, -0.08463612, -0.08016039, ...,  1.        ,
         8.        ,  4.        ],
       [-0.87256276,  0.23325479, -0.05811155, ...,  1.        ,
         8.        ,  4.        ]])

cols = pre1.get_feature_names_out()
cols

array(['num__MSSubClass', 'num__LotFrontage', 'num__LotArea',
       'num__OverallQual', 'num__OverallCond', 'num__YearBuilt',
       'num__YearRemodAdd', 'num__MasVnrArea', 'num__BsmtFinSF1',
       'num__BsmtFinSF2', 'num__BsmtUnfSF', 'num__TotalBsmtSF',
       'num__1stFlrSF', 'num__2ndFlrSF', 'num__LowQualFinSF',
       'num__GrLivArea', 'num__BsmtFullBath', 'num__BsmtHalfBath',
       'num__FullBath', 'num__HalfBath', 'num__BedroomAbvGr',
       'num__KitchenAbvGr', 'num__TotRmsAbvGrd', 'num__Fireplaces',
       'num__GarageYrBlt', 'num__GarageCars', 'num__GarageArea',
       'num__WoodDeckSF', 'num__OpenPorchSF', 'num__EnclosedPorch',
       'num__3SsnPorch', 'num__ScreenPorch', 'num__PoolArea',
       'num__MiscVal', 'num__MoSold', 'num__YrSold', 'cat__MSZoning',
       'cat__Street', 'cat__Alley', 'cat__LotShape', 'cat__LandContour',
       'cat__Utilities', 'cat__LotConfig', 'cat__LandSlope',
       'cat__Neighborhood', 'cat__Condition1', 'cat__Condition2',
       'cat__BldgType', 'cat__HouseStyle', 'cat__RoofStyle',
       'cat__RoofMatl', 'cat__Exterior1st', 'cat__Exterior2nd',
       'cat__MasVnrType', 'cat__ExterQual', 'cat__ExterCond',
       'cat__Foundation', 'cat__BsmtQual', 'cat__BsmtCond',
       'cat__BsmtExposure', 'cat__BsmtFinType1', 'cat__BsmtFinType2',
       'cat__Heating', 'cat__HeatingQC', 'cat__CentralAir',
       'cat__Electrical', 'cat__KitchenQual', 'cat__Functional',
       'cat__FireplaceQu', 'cat__GarageType', 'cat__GarageFinish',
       'cat__GarageQual', 'cat__GarageCond', 'cat__PavedDrive',
       'cat__PoolQC', 'cat__Fence', 'cat__MiscFeature', 'cat__SaleType',
       'cat__SaleCondition'], dtype=object)

X_pre = pd.DataFrame(X_pre, columns=cols)
X_pre.head()

	num__MSSubClass	num__LotFrontage	num__LotArea	num__OverallQual	num__OverallCond	num__YearBuilt	num__YearRemodAdd	num__MasVnrArea	num__BsmtFinSF1	num__BsmtFinSF2	...	cat__GarageType	cat__GarageFinish	cat__GarageQual	cat__GarageCond	cat__PavedDrive	cat__PoolQC	cat__Fence	cat__MiscFeature	cat__SaleType	cat__SaleCondition
0	0.073375	-0.220875	-0.207142	0.651479	-0.517200	1.050994	0.878668	0.514104	0.575425	-0.288653	...	1.0	2.0	5.0	5.0	2.0	3.0	4.0	1.0	8.0	4.0
1	-0.872563	0.460320	-0.091886	-0.071836	2.179628	0.156734	-0.429577	-0.570750	1.171992	-0.288653	...	1.0	2.0	5.0	5.0	2.0	3.0	4.0	1.0	8.0	4.0
2	0.073375	-0.084636	0.073480	0.651479	-0.517200	0.984752	0.830215	0.325915	0.092907	-0.288653	...	1.0	2.0	5.0	5.0	2.0	3.0	4.0	1.0	8.0	4.0
3	0.309859	-0.447940	-0.096897	0.651479	-0.517200	-1.863632	-0.720298	-0.570750	-0.499274	-0.288653	...	5.0	3.0	5.0	5.0	2.0	3.0	4.0	1.0	8.0	0.0
4	0.073375	0.641972	0.375148	1.374795	-0.517200	0.951632	0.733308	1.366489	0.463568	-0.288653	...	1.0	2.0	5.0	5.0	2.0	3.0	4.0	1.0	8.0	4.0

5 rows × 79 columns

Perform Feature Selection with LinearRegression

from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import SequentialFeatureSelector

lr = LinearRegression()
sel = SequentialFeatureSelector(lr, direction='backward')
sel_features = sel.fit_transform(X_pre, Y)
sel_cols = sel.get_feature_names_out()
sel_cols

array(['num__MSSubClass', 'num__LotArea', 'num__OverallQual',
       'num__OverallCond', 'num__YearBuilt', 'num__MasVnrArea',
       'num__BsmtFinSF1', 'num__BsmtFinSF2', 'num__BsmtUnfSF',
       'num__TotalBsmtSF', 'num__1stFlrSF', 'num__2ndFlrSF',
       'num__BsmtFullBath', 'num__KitchenAbvGr', 'num__TotRmsAbvGrd',
       'num__Fireplaces', 'num__GarageCars', 'num__WoodDeckSF',
       'num__EnclosedPorch', 'num__ScreenPorch', 'num__YrSold',
       'cat__Alley', 'cat__LandContour', 'cat__Neighborhood',
       'cat__BldgType', 'cat__HouseStyle', 'cat__RoofMatl',
       'cat__MasVnrType', 'cat__ExterQual', 'cat__ExterCond',
       'cat__BsmtQual', 'cat__BsmtCond', 'cat__BsmtExposure',
       'cat__HeatingQC', 'cat__KitchenQual', 'cat__Functional',
       'cat__GarageFinish', 'cat__GarageCond', 'cat__Fence',
       'cat__SaleCondition'], dtype=object)

len(sel_cols)

40

imp_cols = []
for i in sel_cols:
    col = i.split('__')[1]
    imp_cols.append(col)

imp_cols

['MSSubClass',
 'LotArea',
 'OverallQual',
 'OverallCond',
 'YearBuilt',
 'MasVnrArea',
 'BsmtFinSF1',
 'BsmtFinSF2',
 'BsmtUnfSF',
 'TotalBsmtSF',
 '1stFlrSF',
 '2ndFlrSF',
 'BsmtFullBath',
 'KitchenAbvGr',
 'TotRmsAbvGrd',
 'Fireplaces',
 'GarageCars',
 'WoodDeckSF',
 'EnclosedPorch',
 'ScreenPorch',
 'YrSold',
 'Alley',
 'LandContour',
 'Neighborhood',
 'BldgType',
 'HouseStyle',
 'RoofMatl',
 'MasVnrType',
 'ExterQual',
 'ExterCond',
 'BsmtQual',
 'BsmtCond',
 'BsmtExposure',
 'HeatingQC',
 'KitchenQual',
 'Functional',
 'GarageFinish',
 'GarageCond',
 'Fence',
 'SaleCondition']

X_sel = X[imp_cols]
X_sel

	MSSubClass	LotArea	OverallQual	OverallCond	YearBuilt	MasVnrArea	BsmtFinSF1	BsmtFinSF2	BsmtUnfSF	TotalBsmtSF	...	BsmtQual	BsmtCond	BsmtExposure	HeatingQC	KitchenQual	Functional	GarageFinish	GarageCond	Fence	SaleCondition
0	60	8450	7	5	2003	196.0	706	0	150	856	...	Gd	TA	No	Ex	Gd	Typ	RFn	TA	NaN	Normal
1	20	9600	6	8	1976	0.0	978	0	284	1262	...	Gd	TA	Gd	Ex	TA	Typ	RFn	TA	NaN	Normal
2	60	11250	7	5	2001	162.0	486	0	434	920	...	Gd	TA	Mn	Ex	Gd	Typ	RFn	TA	NaN	Normal
3	70	9550	7	5	1915	0.0	216	0	540	756	...	TA	Gd	No	Gd	Gd	Typ	Unf	TA	NaN	Abnorml
4	60	14260	8	5	2000	350.0	655	0	490	1145	...	Gd	TA	Av	Ex	Gd	Typ	RFn	TA	NaN	Normal
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1455	60	7917	6	5	1999	0.0	0	0	953	953	...	Gd	TA	No	Ex	TA	Typ	RFn	TA	NaN	Normal
1456	20	13175	6	6	1978	119.0	790	163	589	1542	...	Gd	TA	No	TA	TA	Min1	Unf	TA	MnPrv	Normal
1457	70	9042	7	9	1941	0.0	275	0	877	1152	...	TA	Gd	No	Ex	Gd	Typ	RFn	TA	GdPrv	Normal
1458	20	9717	5	6	1950	0.0	49	1029	0	1078	...	TA	TA	Mn	Gd	Gd	Typ	Unf	TA	NaN	Normal
1459	20	9937	5	6	1965	0.0	830	290	136	1256	...	TA	TA	No	Gd	TA	Typ	Fin	TA	NaN	Normal

1460 rows × 40 columns

Create Final Pipeline

cat_sel = list(X_sel.columns[X_sel.dtypes=='object'])
con_sel = list(X_sel.columns[X_sel.dtypes!='object'])

cat_sel

['Alley',
 'LandContour',
 'Neighborhood',
 'BldgType',
 'HouseStyle',
 'RoofMatl',
 'MasVnrType',
 'ExterQual',
 'ExterCond',
 'BsmtQual',
 'BsmtCond',
 'BsmtExposure',
 'HeatingQC',
 'KitchenQual',
 'Functional',
 'GarageFinish',
 'GarageCond',
 'Fence',
 'SaleCondition']

con_sel

['MSSubClass',
 'LotArea',
 'OverallQual',
 'OverallCond',
 'YearBuilt',
 'MasVnrArea',
 'BsmtFinSF1',
 'BsmtFinSF2',
 'BsmtUnfSF',
 'TotalBsmtSF',
 '1stFlrSF',
 '2ndFlrSF',
 'BsmtFullBath',
 'KitchenAbvGr',
 'TotRmsAbvGrd',
 'Fireplaces',
 'GarageCars',
 'WoodDeckSF',
 'EnclosedPorch',
 'ScreenPorch',
 'YrSold']

from sklearn.preprocessing import OneHotEncoder

num_pipe2 = Pipeline(steps=[('impute', SimpleImputer(strategy='median')),
                            ('scaler', StandardScaler())])

cat_pipe2 = Pipeline(steps=[('impute', SimpleImputer(strategy='constant', fill_value='NotAvailable')),
                            ('ohe', OneHotEncoder(handle_unknown='ignore'))])

pre2 = ColumnTransformer([('num', num_pipe2, con_sel),
                          ('cat', cat_pipe2, cat_sel)])

X_sel_pre = pre2.fit_transform(X_sel).toarray()
X_sel_pre

array([[ 0.07337496, -0.20714171,  0.65147924, ...,  0.        ,
         1.        ,  0.        ],
       [-0.87256276, -0.09188637, -0.07183611, ...,  0.        ,
         1.        ,  0.        ],
       [ 0.07337496,  0.07347998,  0.65147924, ...,  0.        ,
         1.        ,  0.        ],
       ...,
       [ 0.30985939, -0.14781027,  0.65147924, ...,  0.        ,
         1.        ,  0.        ],
       [-0.87256276, -0.08016039, -0.79515147, ...,  0.        ,
         1.        ,  0.        ],
       [-0.87256276, -0.05811155, -0.79515147, ...,  0.        ,
         1.        ,  0.        ]])

final_cols = pre2.get_feature_names_out()
final_cols

array(['num__MSSubClass', 'num__LotArea', 'num__OverallQual',
       'num__OverallCond', 'num__YearBuilt', 'num__MasVnrArea',
       'num__BsmtFinSF1', 'num__BsmtFinSF2', 'num__BsmtUnfSF',
       'num__TotalBsmtSF', 'num__1stFlrSF', 'num__2ndFlrSF',
       'num__BsmtFullBath', 'num__KitchenAbvGr', 'num__TotRmsAbvGrd',
       'num__Fireplaces', 'num__GarageCars', 'num__WoodDeckSF',
       'num__EnclosedPorch', 'num__ScreenPorch', 'num__YrSold',
       'cat__Alley_Grvl', 'cat__Alley_NotAvailable', 'cat__Alley_Pave',
       'cat__LandContour_Bnk', 'cat__LandContour_HLS',
       'cat__LandContour_Low', 'cat__LandContour_Lvl',
       'cat__Neighborhood_Blmngtn', 'cat__Neighborhood_Blueste',
       'cat__Neighborhood_BrDale', 'cat__Neighborhood_BrkSide',
       'cat__Neighborhood_ClearCr', 'cat__Neighborhood_CollgCr',
       'cat__Neighborhood_Crawfor', 'cat__Neighborhood_Edwards',
       'cat__Neighborhood_Gilbert', 'cat__Neighborhood_IDOTRR',
       'cat__Neighborhood_MeadowV', 'cat__Neighborhood_Mitchel',
       'cat__Neighborhood_NAmes', 'cat__Neighborhood_NPkVill',
       'cat__Neighborhood_NWAmes', 'cat__Neighborhood_NoRidge',
       'cat__Neighborhood_NridgHt', 'cat__Neighborhood_OldTown',
       'cat__Neighborhood_SWISU', 'cat__Neighborhood_Sawyer',
       'cat__Neighborhood_SawyerW', 'cat__Neighborhood_Somerst',
       'cat__Neighborhood_StoneBr', 'cat__Neighborhood_Timber',
       'cat__Neighborhood_Veenker', 'cat__BldgType_1Fam',
       'cat__BldgType_2fmCon', 'cat__BldgType_Duplex',
       'cat__BldgType_Twnhs', 'cat__BldgType_TwnhsE',
       'cat__HouseStyle_1.5Fin', 'cat__HouseStyle_1.5Unf',
       'cat__HouseStyle_1Story', 'cat__HouseStyle_2.5Fin',
       'cat__HouseStyle_2.5Unf', 'cat__HouseStyle_2Story',
       'cat__HouseStyle_SFoyer', 'cat__HouseStyle_SLvl',
       'cat__RoofMatl_ClyTile', 'cat__RoofMatl_CompShg',
       'cat__RoofMatl_Membran', 'cat__RoofMatl_Metal',
       'cat__RoofMatl_Roll', 'cat__RoofMatl_Tar&Grv',
       'cat__RoofMatl_WdShake', 'cat__RoofMatl_WdShngl',
       'cat__MasVnrType_BrkCmn', 'cat__MasVnrType_BrkFace',
       'cat__MasVnrType_NotAvailable', 'cat__MasVnrType_Stone',
       'cat__ExterQual_Ex', 'cat__ExterQual_Fa', 'cat__ExterQual_Gd',
       'cat__ExterQual_TA', 'cat__ExterCond_Ex', 'cat__ExterCond_Fa',
       'cat__ExterCond_Gd', 'cat__ExterCond_Po', 'cat__ExterCond_TA',
       'cat__BsmtQual_Ex', 'cat__BsmtQual_Fa', 'cat__BsmtQual_Gd',
       'cat__BsmtQual_NotAvailable', 'cat__BsmtQual_TA',
       'cat__BsmtCond_Fa', 'cat__BsmtCond_Gd',
       'cat__BsmtCond_NotAvailable', 'cat__BsmtCond_Po',
       'cat__BsmtCond_TA', 'cat__BsmtExposure_Av', 'cat__BsmtExposure_Gd',
       'cat__BsmtExposure_Mn', 'cat__BsmtExposure_No',
       'cat__BsmtExposure_NotAvailable', 'cat__HeatingQC_Ex',
       'cat__HeatingQC_Fa', 'cat__HeatingQC_Gd', 'cat__HeatingQC_Po',
       'cat__HeatingQC_TA', 'cat__KitchenQual_Ex', 'cat__KitchenQual_Fa',
       'cat__KitchenQual_Gd', 'cat__KitchenQual_TA',
       'cat__Functional_Maj1', 'cat__Functional_Maj2',
       'cat__Functional_Min1', 'cat__Functional_Min2',
       'cat__Functional_Mod', 'cat__Functional_Sev',
       'cat__Functional_Typ', 'cat__GarageFinish_Fin',
       'cat__GarageFinish_NotAvailable', 'cat__GarageFinish_RFn',
       'cat__GarageFinish_Unf', 'cat__GarageCond_Ex',
       'cat__GarageCond_Fa', 'cat__GarageCond_Gd',
       'cat__GarageCond_NotAvailable', 'cat__GarageCond_Po',
       'cat__GarageCond_TA', 'cat__Fence_GdPrv', 'cat__Fence_GdWo',
       'cat__Fence_MnPrv', 'cat__Fence_MnWw', 'cat__Fence_NotAvailable',
       'cat__SaleCondition_Abnorml', 'cat__SaleCondition_AdjLand',
       'cat__SaleCondition_Alloca', 'cat__SaleCondition_Family',
       'cat__SaleCondition_Normal', 'cat__SaleCondition_Partial'],
      dtype=object)

X_sel_pre = pd.DataFrame(X_sel_pre, columns=final_cols)
X_sel_pre.head()

	num__MSSubClass	num__LotArea	num__OverallQual	num__OverallCond	num__YearBuilt	num__MasVnrArea	num__BsmtFinSF1	num__BsmtFinSF2	num__BsmtUnfSF	num__TotalBsmtSF	...	cat__Fence_GdWo	cat__Fence_MnPrv	cat__Fence_MnWw	cat__Fence_NotAvailable	cat__SaleCondition_Abnorml	cat__SaleCondition_AdjLand	cat__SaleCondition_Alloca	cat__SaleCondition_Family	cat__SaleCondition_Normal	cat__SaleCondition_Partial
0	0.073375	-0.207142	0.651479	-0.517200	1.050994	0.514104	0.575425	-0.288653	-0.944591	-0.459303	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
1	-0.872563	-0.091886	-0.071836	2.179628	0.156734	-0.570750	1.171992	-0.288653	-0.641228	0.466465	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
2	0.073375	0.073480	0.651479	-0.517200	0.984752	0.325915	0.092907	-0.288653	-0.301643	-0.313369	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
3	0.309859	-0.096897	0.651479	-0.517200	-1.863632	-0.570750	-0.499274	-0.288653	-0.061670	-0.687324	...	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0
4	0.073375	0.375148	1.374795	-0.517200	0.951632	1.366489	0.463568	-0.288653	-0.174865	0.199680	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0

5 rows × 139 columns

train Test Split

from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(X_sel_pre, Y, test_size=0.2, random_state=42)

xtrain.shape

(1168, 139)

xtest.shape

(292, 139)

Built A linear model

model = LinearRegression()
model.fit(xtrain, ytrain)

LinearRegression()

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model.score(xtrain, ytrain)

0.9051756618987917

model.score(xtest, ytest)

-8.38311961342074e+18

from RegPackage import evaluate_model
evaluate_model(xtrain, ytrain, xtest, ytest, model)

Training Results : 
MSE : 565584541.83
RMSE: 23782.02
MAE : 14554.79
R2  : 0.9052

====================================

Testing Results : 
MSE : 64301262068805499080576860160.00
RMSE: 253576935206665.62
MAE : 17274158858732.48
R2  : -8383119613420739584.0000

Apply Ridge with Cross Validation

params = {'alpha': np.arange(0.1, 100, 0.1)}
params

{'alpha': array([ 0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,
         1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,
         2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,
         3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,
         4.5,  4.6,  4.7,  4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,  5.5,
         5.6,  5.7,  5.8,  5.9,  6. ,  6.1,  6.2,  6.3,  6.4,  6.5,  6.6,
         6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,  7.4,  7.5,  7.6,  7.7,
         7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,  8.6,  8.7,  8.8,
         8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,  9.8,  9.9,
        10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9, 11. ,
        11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9, 12. , 12.1,
        12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9, 13. , 13.1, 13.2,
        13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9, 14. , 14.1, 14.2, 14.3,
        14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15. , 15.1, 15.2, 15.3, 15.4,
        15.5, 15.6, 15.7, 15.8, 15.9, 16. , 16.1, 16.2, 16.3, 16.4, 16.5,
        16.6, 16.7, 16.8, 16.9, 17. , 17.1, 17.2, 17.3, 17.4, 17.5, 17.6,
        17.7, 17.8, 17.9, 18. , 18.1, 18.2, 18.3, 18.4, 18.5, 18.6, 18.7,
        18.8, 18.9, 19. , 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8,
        19.9, 20. , 20.1, 20.2, 20.3, 20.4, 20.5, 20.6, 20.7, 20.8, 20.9,
        21. , 21.1, 21.2, 21.3, 21.4, 21.5, 21.6, 21.7, 21.8, 21.9, 22. ,
        22.1, 22.2, 22.3, 22.4, 22.5, 22.6, 22.7, 22.8, 22.9, 23. , 23.1,
        23.2, 23.3, 23.4, 23.5, 23.6, 23.7, 23.8, 23.9, 24. , 24.1, 24.2,
        24.3, 24.4, 24.5, 24.6, 24.7, 24.8, 24.9, 25. , 25.1, 25.2, 25.3,
        25.4, 25.5, 25.6, 25.7, 25.8, 25.9, 26. , 26.1, 26.2, 26.3, 26.4,
        26.5, 26.6, 26.7, 26.8, 26.9, 27. , 27.1, 27.2, 27.3, 27.4, 27.5,
        27.6, 27.7, 27.8, 27.9, 28. , 28.1, 28.2, 28.3, 28.4, 28.5, 28.6,
        28.7, 28.8, 28.9, 29. , 29.1, 29.2, 29.3, 29.4, 29.5, 29.6, 29.7,
        29.8, 29.9, 30. , 30.1, 30.2, 30.3, 30.4, 30.5, 30.6, 30.7, 30.8,
        30.9, 31. , 31.1, 31.2, 31.3, 31.4, 31.5, 31.6, 31.7, 31.8, 31.9,
        32. , 32.1, 32.2, 32.3, 32.4, 32.5, 32.6, 32.7, 32.8, 32.9, 33. ,
        33.1, 33.2, 33.3, 33.4, 33.5, 33.6, 33.7, 33.8, 33.9, 34. , 34.1,
        34.2, 34.3, 34.4, 34.5, 34.6, 34.7, 34.8, 34.9, 35. , 35.1, 35.2,
        35.3, 35.4, 35.5, 35.6, 35.7, 35.8, 35.9, 36. , 36.1, 36.2, 36.3,
        36.4, 36.5, 36.6, 36.7, 36.8, 36.9, 37. , 37.1, 37.2, 37.3, 37.4,
        37.5, 37.6, 37.7, 37.8, 37.9, 38. , 38.1, 38.2, 38.3, 38.4, 38.5,
        38.6, 38.7, 38.8, 38.9, 39. , 39.1, 39.2, 39.3, 39.4, 39.5, 39.6,
        39.7, 39.8, 39.9, 40. , 40.1, 40.2, 40.3, 40.4, 40.5, 40.6, 40.7,
        40.8, 40.9, 41. , 41.1, 41.2, 41.3, 41.4, 41.5, 41.6, 41.7, 41.8,
        41.9, 42. , 42.1, 42.2, 42.3, 42.4, 42.5, 42.6, 42.7, 42.8, 42.9,
        43. , 43.1, 43.2, 43.3, 43.4, 43.5, 43.6, 43.7, 43.8, 43.9, 44. ,
        44.1, 44.2, 44.3, 44.4, 44.5, 44.6, 44.7, 44.8, 44.9, 45. , 45.1,
        45.2, 45.3, 45.4, 45.5, 45.6, 45.7, 45.8, 45.9, 46. , 46.1, 46.2,
        46.3, 46.4, 46.5, 46.6, 46.7, 46.8, 46.9, 47. , 47.1, 47.2, 47.3,
        47.4, 47.5, 47.6, 47.7, 47.8, 47.9, 48. , 48.1, 48.2, 48.3, 48.4,
        48.5, 48.6, 48.7, 48.8, 48.9, 49. , 49.1, 49.2, 49.3, 49.4, 49.5,
        49.6, 49.7, 49.8, 49.9, 50. , 50.1, 50.2, 50.3, 50.4, 50.5, 50.6,
        50.7, 50.8, 50.9, 51. , 51.1, 51.2, 51.3, 51.4, 51.5, 51.6, 51.7,
        51.8, 51.9, 52. , 52.1, 52.2, 52.3, 52.4, 52.5, 52.6, 52.7, 52.8,
        52.9, 53. , 53.1, 53.2, 53.3, 53.4, 53.5, 53.6, 53.7, 53.8, 53.9,
        54. , 54.1, 54.2, 54.3, 54.4, 54.5, 54.6, 54.7, 54.8, 54.9, 55. ,
        55.1, 55.2, 55.3, 55.4, 55.5, 55.6, 55.7, 55.8, 55.9, 56. , 56.1,
        56.2, 56.3, 56.4, 56.5, 56.6, 56.7, 56.8, 56.9, 57. , 57.1, 57.2,
        57.3, 57.4, 57.5, 57.6, 57.7, 57.8, 57.9, 58. , 58.1, 58.2, 58.3,
        58.4, 58.5, 58.6, 58.7, 58.8, 58.9, 59. , 59.1, 59.2, 59.3, 59.4,
        59.5, 59.6, 59.7, 59.8, 59.9, 60. , 60.1, 60.2, 60.3, 60.4, 60.5,
        60.6, 60.7, 60.8, 60.9, 61. , 61.1, 61.2, 61.3, 61.4, 61.5, 61.6,
        61.7, 61.8, 61.9, 62. , 62.1, 62.2, 62.3, 62.4, 62.5, 62.6, 62.7,
        62.8, 62.9, 63. , 63.1, 63.2, 63.3, 63.4, 63.5, 63.6, 63.7, 63.8,
        63.9, 64. , 64.1, 64.2, 64.3, 64.4, 64.5, 64.6, 64.7, 64.8, 64.9,
        65. , 65.1, 65.2, 65.3, 65.4, 65.5, 65.6, 65.7, 65.8, 65.9, 66. ,
        66.1, 66.2, 66.3, 66.4, 66.5, 66.6, 66.7, 66.8, 66.9, 67. , 67.1,
        67.2, 67.3, 67.4, 67.5, 67.6, 67.7, 67.8, 67.9, 68. , 68.1, 68.2,
        68.3, 68.4, 68.5, 68.6, 68.7, 68.8, 68.9, 69. , 69.1, 69.2, 69.3,
        69.4, 69.5, 69.6, 69.7, 69.8, 69.9, 70. , 70.1, 70.2, 70.3, 70.4,
        70.5, 70.6, 70.7, 70.8, 70.9, 71. , 71.1, 71.2, 71.3, 71.4, 71.5,
        71.6, 71.7, 71.8, 71.9, 72. , 72.1, 72.2, 72.3, 72.4, 72.5, 72.6,
        72.7, 72.8, 72.9, 73. , 73.1, 73.2, 73.3, 73.4, 73.5, 73.6, 73.7,
        73.8, 73.9, 74. , 74.1, 74.2, 74.3, 74.4, 74.5, 74.6, 74.7, 74.8,
        74.9, 75. , 75.1, 75.2, 75.3, 75.4, 75.5, 75.6, 75.7, 75.8, 75.9,
        76. , 76.1, 76.2, 76.3, 76.4, 76.5, 76.6, 76.7, 76.8, 76.9, 77. ,
        77.1, 77.2, 77.3, 77.4, 77.5, 77.6, 77.7, 77.8, 77.9, 78. , 78.1,
        78.2, 78.3, 78.4, 78.5, 78.6, 78.7, 78.8, 78.9, 79. , 79.1, 79.2,
        79.3, 79.4, 79.5, 79.6, 79.7, 79.8, 79.9, 80. , 80.1, 80.2, 80.3,
        80.4, 80.5, 80.6, 80.7, 80.8, 80.9, 81. , 81.1, 81.2, 81.3, 81.4,
        81.5, 81.6, 81.7, 81.8, 81.9, 82. , 82.1, 82.2, 82.3, 82.4, 82.5,
        82.6, 82.7, 82.8, 82.9, 83. , 83.1, 83.2, 83.3, 83.4, 83.5, 83.6,
        83.7, 83.8, 83.9, 84. , 84.1, 84.2, 84.3, 84.4, 84.5, 84.6, 84.7,
        84.8, 84.9, 85. , 85.1, 85.2, 85.3, 85.4, 85.5, 85.6, 85.7, 85.8,
        85.9, 86. , 86.1, 86.2, 86.3, 86.4, 86.5, 86.6, 86.7, 86.8, 86.9,
        87. , 87.1, 87.2, 87.3, 87.4, 87.5, 87.6, 87.7, 87.8, 87.9, 88. ,
        88.1, 88.2, 88.3, 88.4, 88.5, 88.6, 88.7, 88.8, 88.9, 89. , 89.1,
        89.2, 89.3, 89.4, 89.5, 89.6, 89.7, 89.8, 89.9, 90. , 90.1, 90.2,
        90.3, 90.4, 90.5, 90.6, 90.7, 90.8, 90.9, 91. , 91.1, 91.2, 91.3,
        91.4, 91.5, 91.6, 91.7, 91.8, 91.9, 92. , 92.1, 92.2, 92.3, 92.4,
        92.5, 92.6, 92.7, 92.8, 92.9, 93. , 93.1, 93.2, 93.3, 93.4, 93.5,
        93.6, 93.7, 93.8, 93.9, 94. , 94.1, 94.2, 94.3, 94.4, 94.5, 94.6,
        94.7, 94.8, 94.9, 95. , 95.1, 95.2, 95.3, 95.4, 95.5, 95.6, 95.7,
        95.8, 95.9, 96. , 96.1, 96.2, 96.3, 96.4, 96.5, 96.6, 96.7, 96.8,
        96.9, 97. , 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.9,
        98. , 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99. ,
        99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9])}

from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
rr = Ridge()
gscv1 = GridSearchCV(rr, param_grid=params, cv=5, scoring='neg_mean_squared_error')
gscv1.fit(xtrain, ytrain)

GridSearchCV(cv=5, estimator=Ridge(),
             param_grid={'alpha': array([ 0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,  1.1,
        1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,  2.2,
        2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,
        3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,
        4.5,  4.6,  4.7,  4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,  5.5,
        5.6,  5.7,  5.8,  5.9,  6. ,  6.1,  6.2,  6.3,  6.4,  6.5,  6.6,
        6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,  7....
       93.6, 93.7, 93.8, 93.9, 94. , 94.1, 94.2, 94.3, 94.4, 94.5, 94.6,
       94.7, 94.8, 94.9, 95. , 95.1, 95.2, 95.3, 95.4, 95.5, 95.6, 95.7,
       95.8, 95.9, 96. , 96.1, 96.2, 96.3, 96.4, 96.5, 96.6, 96.7, 96.8,
       96.9, 97. , 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.9,
       98. , 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99. ,
       99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9])},
             scoring='neg_mean_squared_error')

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gscv1.best_params_

{'alpha': 1.3000000000000003}

gscv1.best_score_

-1025505431.8491377

best_ridge = gscv1.best_estimator_
best_ridge

Ridge(alpha=1.3000000000000003)

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best_ridge.score(xtrain, ytrain)

0.8938175920123184

best_ridge.score(xtest, ytest)

0.8898912783626156

evaluate_model(xtrain, ytrain, xtest, ytest, best_ridge)

Training Results : 
MSE : 633330321.89
RMSE: 25166.05
MAE : 15450.20
R2  : 0.8938

====================================

Testing Results : 
MSE : 844569813.22
RMSE: 29061.48
MAE : 17774.83
R2  : 0.8899

Apply Lasso with Cross Validation

params2 = {'alpha':np.arange(180, 200, 0.1)}
params2

{'alpha': array([180. , 180.1, 180.2, 180.3, 180.4, 180.5, 180.6, 180.7, 180.8,
        180.9, 181. , 181.1, 181.2, 181.3, 181.4, 181.5, 181.6, 181.7,
        181.8, 181.9, 182. , 182.1, 182.2, 182.3, 182.4, 182.5, 182.6,
        182.7, 182.8, 182.9, 183. , 183.1, 183.2, 183.3, 183.4, 183.5,
        183.6, 183.7, 183.8, 183.9, 184. , 184.1, 184.2, 184.3, 184.4,
        184.5, 184.6, 184.7, 184.8, 184.9, 185. , 185.1, 185.2, 185.3,
        185.4, 185.5, 185.6, 185.7, 185.8, 185.9, 186. , 186.1, 186.2,
        186.3, 186.4, 186.5, 186.6, 186.7, 186.8, 186.9, 187. , 187.1,
        187.2, 187.3, 187.4, 187.5, 187.6, 187.7, 187.8, 187.9, 188. ,
        188.1, 188.2, 188.3, 188.4, 188.5, 188.6, 188.7, 188.8, 188.9,
        189. , 189.1, 189.2, 189.3, 189.4, 189.5, 189.6, 189.7, 189.8,
        189.9, 190. , 190.1, 190.2, 190.3, 190.4, 190.5, 190.6, 190.7,
        190.8, 190.9, 191. , 191.1, 191.2, 191.3, 191.4, 191.5, 191.6,
        191.7, 191.8, 191.9, 192. , 192.1, 192.2, 192.3, 192.4, 192.5,
        192.6, 192.7, 192.8, 192.9, 193. , 193.1, 193.2, 193.3, 193.4,
        193.5, 193.6, 193.7, 193.8, 193.9, 194. , 194.1, 194.2, 194.3,
        194.4, 194.5, 194.6, 194.7, 194.8, 194.9, 195. , 195.1, 195.2,
        195.3, 195.4, 195.5, 195.6, 195.7, 195.8, 195.9, 196. , 196.1,
        196.2, 196.3, 196.4, 196.5, 196.6, 196.7, 196.8, 196.9, 197. ,
        197.1, 197.2, 197.3, 197.4, 197.5, 197.6, 197.7, 197.8, 197.9,
        198. , 198.1, 198.2, 198.3, 198.4, 198.5, 198.6, 198.7, 198.8,
        198.9, 199. , 199.1, 199.2, 199.3, 199.4, 199.5, 199.6, 199.7,
        199.8, 199.9])}

from sklearn.linear_model import Lasso
ls = Lasso()
gscv2 = GridSearchCV(ls, param_grid=params2, cv=5, scoring='neg_mean_squared_error')
gscv2.fit(xtrain, ytrain)

GridSearchCV(cv=5, estimator=Lasso(),
             param_grid={'alpha': array([180. , 180.1, 180.2, 180.3, 180.4, 180.5, 180.6, 180.7, 180.8,
       180.9, 181. , 181.1, 181.2, 181.3, 181.4, 181.5, 181.6, 181.7,
       181.8, 181.9, 182. , 182.1, 182.2, 182.3, 182.4, 182.5, 182.6,
       182.7, 182.8, 182.9, 183. , 183.1, 183.2, 183.3, 183.4, 183.5,
       183.6, 183.7, 183.8, 183.9, 184. , 184.1, 184.2, 184.3, 184.4,
       184.5, 184.6, 184.7, 184....
       194.4, 194.5, 194.6, 194.7, 194.8, 194.9, 195. , 195.1, 195.2,
       195.3, 195.4, 195.5, 195.6, 195.7, 195.8, 195.9, 196. , 196.1,
       196.2, 196.3, 196.4, 196.5, 196.6, 196.7, 196.8, 196.9, 197. ,
       197.1, 197.2, 197.3, 197.4, 197.5, 197.6, 197.7, 197.8, 197.9,
       198. , 198.1, 198.2, 198.3, 198.4, 198.5, 198.6, 198.7, 198.8,
       198.9, 199. , 199.1, 199.2, 199.3, 199.4, 199.5, 199.6, 199.7,
       199.8, 199.9])},
             scoring='neg_mean_squared_error')

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gscv2.best_params_

{'alpha': 180.0}

gscv2.best_score_

-1017797737.3011879

best_lasso = gscv2.best_estimator_
best_lasso

Lasso(alpha=180.0)

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best_lasso.score(xtrain, ytrain)

0.8878640283403164

best_lasso.score(xtest, ytest)

0.8884719356566722

evaluate_model(xtrain, ytrain, xtest, ytest, best_lasso)

Training Results : 
MSE : 668840652.35
RMSE: 25861.95
MAE : 15751.48
R2  : 0.8879

====================================

Testing Results : 
MSE : 855456634.77
RMSE: 29248.19
MAE : 17315.94
R2  : 0.8885

Select Lasso model for final prediction

Use sample_set.csv for prediction

xnew = pd.read_csv('sample_set.csv')
xnew.head()

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	1461	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	AllPub	...	120	0	NaN	MnPrv	NaN	0	6	2010	WD	Normal
1	1462	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal
2	1463	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal
3	1464	60	RL	78.0	9978	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	6	2010	WD	Normal
4	1465	120	RL	43.0	5005	Pave	NaN	IR1	HLS	AllPub	...	144	0	NaN	NaN	NaN	0	1	2010	WD	Normal

5 rows × 80 columns

s = xnew.isna().sum()
s[s>0]

MSZoning           4
LotFrontage      227
Alley           1352
Utilities          2
Exterior1st        1
Exterior2nd        1
MasVnrType       894
MasVnrArea        15
BsmtQual          44
BsmtCond          45
BsmtExposure      44
BsmtFinType1      42
BsmtFinSF1         1
BsmtFinType2      42
BsmtFinSF2         1
BsmtUnfSF          1
TotalBsmtSF        1
BsmtFullBath       2
BsmtHalfBath       2
KitchenQual        1
Functional         2
FireplaceQu      730
GarageType        76
GarageYrBlt       78
GarageFinish      78
GarageCars         1
GarageArea         1
GarageQual        78
GarageCond        78
PoolQC          1456
Fence           1169
MiscFeature     1408
SaleType           1
dtype: int64

# Apply pre.transform on out of sample data
xnew_pre = pre2.transform(xnew).toarray()
xnew_pre

array([[-0.87256276,  0.11076257, -0.79515147, ...,  0.        ,
         1.        ,  0.        ],
       [-0.87256276,  0.37584985, -0.07183611, ...,  0.        ,
         1.        ,  0.        ],
       [ 0.07337496,  0.33205282, -0.79515147, ...,  0.        ,
         1.        ,  0.        ],
       ...,
       [-0.87256276,  0.95042275, -0.79515147, ...,  0.        ,
         0.        ,  0.        ],
       [ 0.66458604, -0.00759964, -0.79515147, ...,  0.        ,
         1.        ,  0.        ],
       [ 0.07337496, -0.08918038,  0.65147924, ...,  0.        ,
         1.        ,  0.        ]])

xnew_pre = pd.DataFrame(xnew_pre, columns=final_cols)
xnew_pre.head()

	num__MSSubClass	num__LotArea	num__OverallQual	num__OverallCond	num__YearBuilt	num__MasVnrArea	num__BsmtFinSF1	num__BsmtFinSF2	num__BsmtUnfSF	num__TotalBsmtSF	...	cat__Fence_GdWo	cat__Fence_MnPrv	cat__Fence_MnWw	cat__Fence_NotAvailable	cat__SaleCondition_Abnorml	cat__SaleCondition_AdjLand	cat__SaleCondition_Alloca	cat__SaleCondition_Family	cat__SaleCondition_Normal	cat__SaleCondition_Partial
0	-0.872563	0.110763	-0.795151	0.381743	-0.340077	-0.570750	0.053428	0.604293	-0.672923	-0.400017	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
1	-0.872563	0.375850	-0.071836	0.381743	-0.439440	0.027027	1.051363	-0.288653	-0.365032	0.619239	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
2	0.073375	0.332053	-0.795151	-0.517200	0.852269	-0.570750	0.761852	-0.288653	-0.974021	-0.295127	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
3	0.073375	-0.054002	-0.071836	0.381743	0.885390	-0.460051	0.347326	-0.288653	-0.550672	-0.299687	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
4	1.492282	-0.552407	1.374795	-0.517200	0.686666	-0.570750	-0.396190	-0.288653	1.018211	0.507509	...	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0

5 rows × 139 columns

preds = best_lasso.predict(xnew_pre)
preds

array([118792.07533453, 155091.35908051, 168783.86045418, ...,
       175422.71143984, 110928.46269249, 222777.99541006])

df_final = xnew[['Id']]
df_final

	Id
0	1461
1	1462
2	1463
3	1464
4	1465
...	...
1454	2915
1455	2916
1456	2917
1457	2918
1458	2919

1459 rows × 1 columns

df_final['SalePrice'] = preds
df_final

	Id	SalePrice
0	1461	118792.075335
1	1462	155091.359081
2	1463	168783.860454
3	1464	186538.491042
4	1465	216796.324181
...	...	...
1454	2915	81657.630708
1455	2916	77637.340257
1456	2917	175422.711440
1457	2918	110928.462692
1458	2919	222777.995410

1459 rows × 2 columns

df_final['SalePrice'] = df_final['SalePrice'].apply(round, args=(2,))
df_final

	Id	SalePrice
0	1461	118792.08
1	1462	155091.36
2	1463	168783.86
3	1464	186538.49
4	1465	216796.32
...	...	...
1454	2915	81657.63
1455	2916	77637.34
1456	2917	175422.71
1457	2918	110928.46
1458	2919	222778.00

1459 rows × 2 columns

save above dataframe to HouseSalespricePred.csv

df_final.to_csv('HouseSalespricePred.csv', index=False)